Triple
T12499583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northeastern France |
E298784
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Roubaix |
E223522
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Roubaix | Statement: [Northeastern France, hasMajorCity, Roubaix]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roubaix Context triple: [Northeastern France, hasMajorCity, Roubaix]
-
A.
Roubaix
chosen
Roubaix is a city in northern France known for its textile industry heritage and as the finish of the Paris–Roubaix professional cycling race.
-
B.
La Route des Flandres
La Route des Flandres is a 1960 novel by French writer Claude Simon, noted for its fragmented, stream-of-consciousness portrayal of soldiers’ experiences during World War II and considered a key work of the Nouveau Roman movement.
-
C.
Côte de Beauté
Côte de Beauté is a scenic stretch of Atlantic coastline in southwestern France, known for its sandy beaches, seaside resorts, and mild climate.
-
D.
Balignicourt
Balignicourt is a small commune in the Aube department of north-central France.
-
E.
Lille-Flandres
Lille-Flandres is the main railway station in Lille, France, serving as a major regional and national rail hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dfa98348190b9ac164ecdada6fe |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bb131608190b34a07a7026b160e |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:57 p.m.